The ogbl-collab and ogbl-citation2 are two datasets for link prediction. The challenge leaderboard can be checked at: https://ogb.stanford.edu/docs/leader_linkprop/. We apply feature propagation to solve this challenge and this repo contains our code submission. The technical report can be checked at ogb_report.pdf
Install base packages:
Python==3.6 Pytorch==1.7.1 pytorch_geometric==2.0.1 ogb==1.3.2
Running the default code 10 times, here we present our results on the ogbl-collab and ogbl-citation2.
| Method | ogbl-collab (Hits@50) | ogbl-citation2 (MRR) |
|---|---|---|
| PLNLP | 0.7046 ± 0.0040 | -- |
| PLNLP + SIGN | 0.7087 ± 0.0033 | -- |
| MLP | 0.1991 ± 0.0170 | 0.2900 ± 0.0018 |
| MLP + SIGN | 0.2839 ± 0.0127 | 0.3224 ± 0.0017 |
- PLNLP as backbone
python plnlp_sign.py --data_name=ogbl-collab --predictor=DOT --use_valedges_as_input=True --year=2010 --train_on_subgraph=True --epochs=800 --eval_last_best=True --dropout=0.3 --gnn_num_layers=1 --grad_clip_norm=1 --use_lr_decay=True --random_walk_augment=True --walk_length=10 --loss_func=WeightedHingeAUC --data_path=dataset
- MLP as backbone
python mlp_collab_sign.py
python mlp_citation2_sign.py
- https://ogb.stanford.edu/
- https://github.com/snap-stanford/ogb
- https://github.com/zhitao-wang/PLNLP
- Pairwise Learning for Neural Link Prediction (https://arxiv.org/pdf/2112.02936.pdf)
If you find this work useful, please consider citing the technical report:
@article{yao2022ogb,
title={Technical Report for OGB Link Property Prediction},
author={Yao, Liang and Liu, Qiang and Cai, Hongyun and Ji, Shenggong and He, Feng and Cheng, Xu},
year={2022}
}